一种高效的多层次图像值方法,基于改进的基于堆的优化器
Essam H Houssein1, Gaber M Mohamed2, Ibrahim A Ibrahim2
1Faculty of Computers and Information, Minia University, Minia, Egypt. essam.halim@mu.edu.eg.
Scientific reports
|June 5, 2023
概括
一个改进的基于堆的优化器 (IHBO) 通过降低计算成本来增强多级值 (MTH) 图像分割. 对于精确的图像细分,IHBO在其他元启发算法上表现出卓越的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 图像细分将图像像素分为类别进行对象分析.
- 多级值 (MTH) 要求为有效的细分提供最佳值.
- 像卡普尔和奥苏方法这样的现有方法对于MTH来说在计算上昂贵.
研究的目的:
- 为了解决MTH图像分割的高计算成本.
- 提高基于堆的优化器 (HBO) 的收率和本地搜索效率.
- 为MTH问题引入一个改进的基于堆的优化器 (IHBO).
主要方法:
- 通过将基于对立的学习与HBO集成,开发了一个改进的基于堆的优化器 (IHBO).
- 应用IHBO到MTH图像细分,使用Otsu和Kapur方法作为目标函数.
- 对七个元启发算法进行CEC'2020测试套件的IHBO性能评估.
主要成果:
- 在健身价值方面,IHBO显著超过了基准算法.
- 在结构相似性指数 (SSIM),特征相似性指数 (FSIM) 和峰值信号与噪声比率方面,IHBO取得了卓越的表现.
- 拟议的IHBO方法证明了MTH图像细分的提高效率和准确性.
结论:
- IHBO算法是MTH图像分割的高效和高效的方法.
- IHBO克服了传统MTH技术的计算成本限制.
- 在图像细分任务中,IHBO显示出对现有的元启发算法的优越性.
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